#
Marigold Computer Vision

Marigold is a diffusion-based method and a collection of pipelines designed for dense computer vision tasks, including monocular depth prediction, surface normals estimation, and intrinsic image decomposition.

This guide will walk you through using Marigold to generate fast and high-quality predictions for images and videos.

Each pipeline is tailored for a specific computer vision task, processing an input RGB image and generating a corresponding prediction. Currently, the following computer vision tasks are implemented:

Pipeline | Recommended Model Checkpoints | Spaces (Interactive Apps) | Predicted Modalities
--- | --- | --- | ---
MarigoldDepthPipeline | prs-eth/marigold-depth-v1-1 | Depth Estimation | Depth, Disparity
MarigoldNormalsPipeline | prs-eth/marigold-normals-v1-1 | Surface Normals Estimation | Surface normals
MarigoldIntrinsicsPipeline | prs-eth/marigold-iid-appearance-v1-1, prs-eth/marigold-iid-lighting-v1-1 | Intrinsic Image Decomposition | Albedo, Materials, Lighting

All original checkpoints are available under the PRS-ETH organization on Hugging Face. They are designed for use with diffusers pipelines and the original codebase, which can also be used to train new model checkpoints. The following is a summary of the recommended checkpoints, all of which produce reliable results with 1 to 4 steps.

Checkpoint | Modality | Comment
--- | --- | ---
prs-eth/marigold-depth-v1-1 | Depth | Affine-invariant depth prediction assigns each pixel a value between 0 (near plane) and 1 (far plane), with both planes determined by the model during inference.
prs-eth/marigold-normals-v0-1 | Normals | The surface normals predictions are unit-length 3D vectors in the screen space camera, with values in the range from -1 to 1.
prs-eth/marigold-iid-appearance-v1-1 | Intrinsics | InteriorVerse decomposition is comprised of Albedo and two BRDF material properties: Roughness and Metallicity.
prs-eth/marigold-iid-lighting-v1-1 | Intrinsics | HyperSim decomposition of an imageI I I is comprised of AlbedoA A A, Diffuse shadingS S S, and Non-diffuse residualR R R:I=A∗S+R I = A*S+R I=A∗S+R.

The examples below are mostly given for depth prediction, but they can be universally applied to other supported modalities. We showcase the predictions using the same input image of Albert Einstein generated by Midjourney. This makes it easier to compare visualizations of the predictions across various modalities and checkpoints.

Example input image for all Marigold pipelines

##
Depth Prediction

To get a depth prediction, load the `prs-eth/marigold-depth-v1-1` checkpoint into MarigoldDepthPipeline, put the image through the pipeline, and save the predictions:

    import diffusers
    import torch

    pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
        "prs-eth/marigold-depth-v1-1", variant="fp16", torch_dtype=torch.float16
    ).to("cuda")

    image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")

    depth = pipe(image)

    vis = pipe.image_processor.visualize_depth(depth.prediction)
    vis[0].save("einstein_depth.png")

    depth_16bit = pipe.image_processor.export_depth_to_16bit_png(depth.prediction)
    depth_16bit[0].save("einstein_depth_16bit.png")

The visualize_depth() function applies one of matplotlib’s colormaps (`Spectral` by default) to map the predicted pixel values from a single-channel `[0, 1]` depth range into an RGB image. With the `Spectral` colormap, pixels with near depth are painted red, and far pixels are blue. The 16-bit PNG file stores the single channel values mapped linearly from the `[0, 1]` range into `[0, 65535]`. Below are the raw and the visualized predictions. The darker and closer areas (mustache) are easier to distinguish in the visualization.

Predicted depth (16-bit PNG)
Predicted depth visualization (Spectral)

##
Surface Normals Estimation

Load the `prs-eth/marigold-normals-v1-1` checkpoint into MarigoldNormalsPipeline, put the image through the pipeline, and save the predictions:

    import diffusers
    import torch

    pipe = diffusers.MarigoldNormalsPipeline.from_pretrained(
        "prs-eth/marigold-normals-v1-1", variant="fp16", torch_dtype=torch.float16
    ).to("cuda")

    image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")

    normals = pipe(image)

    vis = pipe.image_processor.visualize_normals(normals.prediction)
    vis[0].save("einstein_normals.png")

The visualize_normals() maps the three-dimensional prediction with pixel values in the range `[-1, 1]` into an RGB image. The visualization function supports flipping surface normals axes to make the visualization compatible with other choices of the frame of reference. Conceptually, each pixel is painted according to the surface normal vector in the frame of reference, where `X` axis points right, `Y` axis points up, and `Z` axis points at the viewer. Below is the visualized prediction:

Predicted surface normals visualization

In this example, the nose tip almost certainly has a point on the surface, in which the surface normal vector points straight at the viewer, meaning that its coordinates are `[0, 0, 1]`. This vector maps to the RGB `[128, 128, 255]`, which corresponds to the violet-blue color. Similarly, a surface normal on the cheek in the right part of the image has a large `X` component, which increases the red hue. Points on the shoulders pointing up with a large `Y` promote green color.

##
Intrinsic Image Decomposition

Marigold provides two models for Intrinsic Image Decomposition (IID): “Appearance” and “Lighting”. Each model produces Albedo maps, derived from InteriorVerse and Hypersim annotations, respectively.

- The “Appearance” model also estimates Material properties: Roughness and Metallicity.
- The “Lighting” model generates Diffuse Shading and Non-diffuse Residual.

Here is the sample code saving predictions made by the “Appearance” model:

    import diffusers
    import torch

    pipe = diffusers.MarigoldIntrinsicsPipeline.from_pretrained(
        "prs-eth/marigold-iid-appearance-v1-1", variant="fp16", torch_dtype=torch.float16
    ).to("cuda")

    image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")

    intrinsics = pipe(image)

    vis = pipe.image_processor.visualize_intrinsics(intrinsics.prediction, pipe.target_properties)
    vis[0]["albedo"].save("einstein_albedo.png")
    vis[0]["roughness"].save("einstein_roughness.png")
    vis[0]["metallicity"].save("einstein_metallicity.png")

Another example demonstrating the predictions made by the “Lighting” model:

    import diffusers
    import torch

    pipe = diffusers.MarigoldIntrinsicsPipeline.from_pretrained(
        "prs-eth/marigold-iid-lighting-v1-1", variant="fp16", torch_dtype=torch.float16
    ).to("cuda")

    image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")

    intrinsics = pipe(image)

    vis = pipe.image_processor.visualize_intrinsics(intrinsics.prediction, pipe.target_properties)
    vis[0]["albedo"].save("einstein_albedo.png")
    vis[0]["shading"].save("einstein_shading.png")
    vis[0]["residual"].save("einstein_residual.png")

Both models share the same pipeline while supporting different decomposition types. The exact decomposition parameterization (e.g., sRGB vs. linear space) is stored in the `pipe.target_properties` dictionary, which is passed into the visualize_intrinsics() function.

Below are some examples showcasing the predicted decomposition outputs. All modalities can be inspected in the Intrinsic Image Decomposition Space.

Predicted albedo ("Appearance" model)
Predicted diffuse shading ("Lighting" model)

##
Speeding up inference

The above quick start snippets are already optimized for quality and speed, loading the checkpoint, utilizing the `fp16` variant of weights and computation, and performing the default number (4) of denoising diffusion steps. The first step to accelerate inference, at the expense of prediction quality, is to reduce the denoising diffusion steps to the minimum:

      import diffusers
      import torch

      pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
          "prs-eth/marigold-depth-v1-1", variant="fp16", torch_dtype=torch.float16
      ).to("cuda")

      image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")

    - depth = pipe(image)
    + depth = pipe(image, num_inference_steps=1)

With this change, the `pipe` call completes in 280ms on RTX 3090 GPU. Internally, the input image is first encoded using the Stable Diffusion VAE encoder, followed by a single denoising step performed by the U-Net. Finally, the prediction latent is decoded with the VAE decoder into pixel space. In this setup, two out of three module calls are dedicated to converting between the pixel and latent spaces of the LDM. Since Marigold’s latent space is compatible with Stable Diffusion 2.0, inference can be accelerated by more than 3x, reducing the call time to 85ms on an RTX 3090, by using a lightweight replacement of the SD VAE. Note that using a lightweight VAE may slightly reduce the visual quality of the predictions.

      import diffusers
      import torch

      pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
          "prs-eth/marigold-depth-v1-1", variant="fp16", torch_dtype=torch.float16
      ).to("cuda")

    + pipe.vae = diffusers.AutoencoderTiny.from_pretrained(
    +     "madebyollin/taesd", torch_dtype=torch.float16
    + ).cuda()

      image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")

      depth = pipe(image, num_inference_steps=1)

So far, we have optimized the number of diffusion steps and model components. Self-attention operations account for a significant portion of computations. Speeding them up can be achieved by using a more efficient attention processor:

      import diffusers
      import torch
    + from diffusers.models.attention_processor import AttnProcessor2_0

      pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
          "prs-eth/marigold-depth-v1-1", variant="fp16", torch_dtype=torch.float16
      ).to("cuda")

    + pipe.vae.set_attn_processor(AttnProcessor2_0())
    + pipe.unet.set_attn_processor(AttnProcessor2_0())

      image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")

      depth = pipe(image, num_inference_steps=1)

Finally, as suggested in Optimizations, enabling `torch.compile` can further enhance performance depending on the target hardware. However, compilation incurs a significant overhead during the first pipeline invocation, making it beneficial only when the same pipeline instance is called repeatedly, such as within a loop.

      import diffusers
      import torch
      from diffusers.models.attention_processor import AttnProcessor2_0

      pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
          "prs-eth/marigold-depth-v1-1", variant="fp16", torch_dtype=torch.float16
      ).to("cuda")

      pipe.vae.set_attn_processor(AttnProcessor2_0())
      pipe.unet.set_attn_processor(AttnProcessor2_0())

    + pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
    + pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)

      image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")

      depth = pipe(image, num_inference_steps=1)

##
Maximizing Precision and Ensembling

Marigold pipelines have a built-in ensembling mechanism combining multiple predictions from different random latents. This is a brute-force way of improving the precision of predictions, capitalizing on the generative nature of diffusion. The ensembling path is activated automatically when the `ensemble_size` argument is set greater or equal than `3`. When aiming for maximum precision, it makes sense to adjust `num_inference_steps` simultaneously with `ensemble_size`. The recommended values vary across checkpoints but primarily depend on the scheduler type. The effect of ensembling is particularly well-seen with surface normals:

      import diffusers

      pipe = diffusers.MarigoldNormalsPipeline.from_pretrained("prs-eth/marigold-normals-v1-1").to("cuda")

      image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")

    - depth = pipe(image)
    + depth = pipe(image, num_inference_steps=10, ensemble_size=5)

      vis = pipe.image_processor.visualize_normals(depth.prediction)
      vis[0].save("einstein_normals.png")

Surface normals, no ensembling
Surface normals, with ensembling

As can be seen, all areas with fine-grained structurers, such as hair, got more conservative and on average more correct predictions. Such a result is more suitable for precision-sensitive downstream tasks, such as 3D reconstruction.

##
Frame-by-frame Video Processing with Temporal Consistency

Due to Marigold’s generative nature, each prediction is unique and defined by the random noise sampled for the latent initialization. This becomes an obvious drawback compared to traditional end-to-end dense regression networks, as exemplified in the following videos:

Input video
Marigold Depth applied to input video frames independently

To address this issue, it is possible to pass `latents` argument to the pipelines, which defines the starting point of diffusion. Empirically, we found that a convex combination of the very same starting point noise latent and the latent corresponding to the previous frame prediction give sufficiently smooth results, as implemented in the snippet below:

    import imageio
    import diffusers
    import torch
    from diffusers.models.attention_processor import AttnProcessor2_0
    from PIL import Image
    from tqdm import tqdm

    device = "cuda"
    path_in = "https://huggingface.co/spaces/prs-eth/marigold-lcm/resolve/c7adb5427947d2680944f898cd91d386bf0d4924/files/video/obama.mp4"
    path_out = "obama_depth.gif"

    pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
        "prs-eth/marigold-depth-v1-1", variant="fp16", torch_dtype=torch.float16
    ).to(device)
    pipe.vae = diffusers.AutoencoderTiny.from_pretrained(
        "madebyollin/taesd", torch_dtype=torch.float16
    ).to(device)
    pipe.unet.set_attn_processor(AttnProcessor2_0())
    pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
    pipe.set_progress_bar_config(disable=True)

    with imageio.get_reader(path_in) as reader:
        size = reader.get_meta_data()['size']
        last_frame_latent = None
        latent_common = torch.randn(
            (1, 4, 768 * size[1] // (8 * max(size)), 768 * size[0] // (8 * max(size)))
        ).to(device=device, dtype=torch.float16)

        out = []
        for frame_id, frame in tqdm(enumerate(reader), desc="Processing Video"):
            frame = Image.fromarray(frame)
            latents = latent_common
            if last_frame_latent is not None:
                latents = 0.9 * latents + 0.1 * last_frame_latent

            depth = pipe(
                frame,
                num_inference_steps=1,
                match_input_resolution=False,
                latents=latents,
                output_latent=True,
            )
            last_frame_latent = depth.latent
            out.append(pipe.image_processor.visualize_depth(depth.prediction)[0])

        diffusers.utils.export_to_gif(out, path_out, fps=reader.get_meta_data()['fps'])

Here, the diffusion process starts from the given computed latent. The pipeline sets `output_latent=True` to access `out.latent` and computes its contribution to the next frame’s latent initialization. The result is much more stable now:

Marigold Depth applied to input video frames independently
Marigold Depth with forced latents initialization

##
Marigold for ControlNet

A very common application for depth prediction with diffusion models comes in conjunction with ControlNet. Depth crispness plays a crucial role in obtaining high-quality results from ControlNet. As seen in comparisons with other methods above, Marigold excels at that task. The snippet below demonstrates how to load an image, compute depth, and pass it into ControlNet in a compatible format:

    import torch
    import diffusers

    device = "cuda"
    generator = torch.Generator(device=device).manual_seed(2024)
    image = diffusers.utils.load_image(
        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_depth_source.png"
    )

    pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
        "prs-eth/marigold-depth-v1-1", torch_dtype=torch.float16, variant="fp16"
    ).to(device)

    depth_image = pipe(image, generator=generator).prediction
    depth_image = pipe.image_processor.visualize_depth(depth_image, color_map="binary")
    depth_image[0].save("motorcycle_controlnet_depth.png")

    controlnet = diffusers.ControlNetModel.from_pretrained(
        "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
    ).to(device)
    pipe = diffusers.StableDiffusionXLControlNetPipeline.from_pretrained(
        "SG161222/RealVisXL_V4.0", torch_dtype=torch.float16, variant="fp16", controlnet=controlnet
    ).to(device)
    pipe.scheduler = diffusers.DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)

    controlnet_out = pipe(
        prompt="high quality photo of a sports bike, city",
        negative_prompt="",
        guidance_scale=6.5,
        num_inference_steps=25,
        image=depth_image,
        controlnet_conditioning_scale=0.7,
        control_guidance_end=0.7,
        generator=generator,
    ).images
    controlnet_out[0].save("motorcycle_controlnet_out.png")

Input image
Depth in the format compatible with ControlNet
ControlNet generation, conditioned on depth and prompt: "high quality photo of a sports bike, city"

##
Quantitative Evaluation

To evaluate Marigold quantitatively in standard leaderboards and benchmarks (such as NYU, KITTI, and other datasets), follow the evaluation protocol outlined in the paper: load the full precision fp32 model and use appropriate values for `num_inference_steps` and `ensemble_size`. Optionally seed randomness to ensure reproducibility. Maximizing `batch_size` will deliver maximum device utilization.

    import diffusers
    import torch

    device = "cuda"
    seed = 2024

    generator = torch.Generator(device=device).manual_seed(seed)
    pipe = diffusers.MarigoldDepthPipeline.from_pretrained("prs-eth/marigold-depth-v1-1").to(device)

    image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")

    depth = pipe(
        image,
        num_inference_steps=4,  # set according to the evaluation protocol from the paper
        ensemble_size=10,       # set according to the evaluation protocol from the paper
        generator=generator,
    )

    # evaluate metrics

##
Using Predictive Uncertainty

The ensembling mechanism built into Marigold pipelines combines multiple predictions obtained from different random latents. As a side effect, it can be used to quantify epistemic (model) uncertainty; simply specify `ensemble_size` greater or equal than 3 and set `output_uncertainty=True`. The resulting uncertainty will be available in the `uncertainty` field of the output. It can be visualized as follows:

    import diffusers
    import torch

    pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
        "prs-eth/marigold-depth-v1-1", variant="fp16", torch_dtype=torch.float16
    ).to("cuda")

    image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")

    depth = pipe(
    	image,
    	ensemble_size=10,  # any number >= 3
    	output_uncertainty=True,
    )

    uncertainty = pipe.image_processor.visualize_uncertainty(depth.uncertainty)
    uncertainty[0].save("einstein_depth_uncertainty.png")

Depth uncertainty
Surface normals uncertainty
Albedo uncertainty

The interpretation of uncertainty is easy: higher values (white) correspond to pixels, where the model struggles to make consistent predictions.

- The depth model exhibits the most uncertainty around discontinuities, where object depth changes abruptly.
- The surface normals model is least confident in fine-grained structures like hair and in dark regions such as the collar area.
- Albedo uncertainty is represented as an RGB image, as it captures uncertainty independently for each color channel, unlike depth and surface normals. It is also higher in shaded regions and at discontinuities.

##
Conclusion

We hope Marigold proves valuable for your downstream tasks, whether as part of a broader generative workflow or for perception-based applications like 3D reconstruction.

Update on GitHub